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Ian Goodfellow: Joint Training Deep Boltzmann Machines for Classification

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Ian Goodfellow: Joint Training Deep Boltzmann Machines for Classification


Published December 12, 2012


Talk by Ian Goodfellow, University of Montreal. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley.

Abstract.
The traditional deep Boltzmann machine training algorithm requires a greedy layerwise pretraining phase. Existing techniques for avoiding greedy pretraining do not perform as well for classification as the layerwise method. I show that 2nd order methods applied to a deterministic training criterion can obtain better classification performance than the existing joint training methods.


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